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"... Abstract: The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representat ..."

Abstract: The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research.

...o action when a novel or familiar stimulus is encountered, it will be important to continue to study in more detail the role of attention (see Kruschke & Blair 2000; Kruschke et al. 2005) and memory (=-=McClelland & Thompson 2007-=-; Vandorpe et al. 2007b) in learning and reasoning. The propositional account that Mitchell et al. advocate also may run into difficulties addressing details of situation-specific response behaviors. ...

...ed to update existing estimates of the most likely model of the world. Work has already begun to relate connectionist and Bayesian accounts, for example in the domain of causal reasoning in children (=-=McClelland & Thompson, 2007-=-). In some cases, connectionism may offer alternative explanations of the same behavior, in others it may be viewed as an implementation of a Bayesian account (see Section 3.1). Connectionism will con...

"... How do we learn causal relations between events from experience? Many have argued for an associative account inspired by animal conditioning models, but there is a growing literature arguing that indirect effects in contingency learning depend on explicit cognitive processes. Our experiments explore ..."

How do we learn causal relations between events from experience? Many have argued for an associative account inspired by animal conditioning models, but there is a growing literature arguing that indirect effects in contingency learning depend on explicit cognitive processes. Our experiments explore the basis of two such effects: blocking and screening off. In Experiment 1, we gave participants an untimed explicit prediction task to replicate standard findings in the contingency learning literature in a novel domain. We obtained robust indirect effects when participants had a causal framework to constrain their reasoning. In Experiment 2, we reduced the time available for explicit recollection by reconstructing the task as a fast-paced RT task. Participants continued to show robust learning of direct relationships, as measured by response times, but there were no indirect effects. Experiment 3 followed up on whether participants in our RT task would produce indirect effects through explicit processes when given an opportunity to make a more deliberative prediction at test.

...s as a basic outcome of the learning process. Another approach has been to argue that retrospective effects are instead driven by the explicit retrieval of memories for previously experienced events (=-=McClelland & Thompson, 2008-=-). More troubling are recent findings that suggest indirect effects are often quite fragile in contingency learning tasks. De Houwer and Beckers (2003) found that blocking was attenuated when particip...

"... Most models of causal reasoning estimate the strength of a causal relation using a function of the proportion of successes and failures: the number of trials on which the cause produced the effect, divided by the total number of trials. Alternatively, people may represent failures as due to a hidden ..."

Most models of causal reasoning estimate the strength of a causal relation using a function of the proportion of successes and failures: the number of trials on which the cause produced the effect, divided by the total number of trials. Alternatively, people may represent failures as due to a hidden inhibitor that has a specific location and extent in time. We model these possibilities, and empirically test a case on which the two models make opposite predictions. We find that children as young as four years old generate responses inconsistent with proportional models, but consistent with an inhibitor-based model. Incorporating a recency component does not help proportional models fit the data.

...y use, or converge to, such a proportion or a function of such a proportion. These include most associative (e.g., Rescorla & Wagner, 1972), causal power (e.g., Cheng, 1997) and neural network (e.g., =-=McClelland & Thompson, 2007-=-) models of causal reasoning. Not all of these models are deeply committed to using simple proportions, but most currently do. We will refer to such models as proportional models. There is an alternat...

...o action when a novel or familiar stimulus is encountered, it will be important to continue to study in more detail the role of attention (see Kruschke & Blair 2000; Kruschke et al. 2005) and memory (=-=McClelland & Thompson 2007-=-; Vandorpe et al. 2007b) in learning and reasoning. The propositional account that Mitchell et al. advocate also may run into difficulties addressing details of situation-specific response behaviors. ...